Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
4
Scientific Computing and Big Data Analysis with Python and Hadoop
Index

Aggregations


Aggregation is the method of collecting data together based on a condition and performing analytics on the data. Aggregation is very important to make sense of data of all sizes as just having raw records of data is not that useful for most use cases.

Note

Imagine a table containing one temperature measurement per day for every city in the world for five years.

For example, if you see the following table and then the aggregated view of the same data then it is obvious that just raw records do not help you understand the data. Shown below is the raw data in the form of a table:

City

Date 

Temperature

Boston

12/23/2016

32

New York

12/24/2016

36

Boston

12/24/2016

30

Philadelphia

12/25/2016

34

Boston

12/25/2016

28

 

Shown below is the average temperature per city:

City

AverageTemperature

Boston 

30 - (32 + 30 + 28)/3

New York

36

Philadelphia

34

Aggregate functions

Aggregations can be performed with the help of functions that can be found in the org.apache.spark.sql.functions package. In addition to this, custom...